During evolution, proteins retain common three-dimensional structural features, even though the underlying sequence of amino acids can diverge dramatically. Such relationships can even be found internally within a given protein structure, arising from duplication and repetition of defined elements. Identifying relationships between two proteins, or two regions of the same protein, that are very distantly related, therefore, can be extremely valuable. Most often, identification of those relationships is needed on the level of primary amino acid codes, which is achieved by aligning their sequences. However, when structures have been determined, such relationships can be detected by overlaying common regions, a technique known as structure alignment. Both procedures involve considerable challenges, especially when the similarities between the two proteins are small. Consequently, there remains a need for methods that reliably and accurately compute sequence or structure alignments. In the past year, we have combined efforts from two different fronts in developing such tools. First, we have made improvements to our benchmark set of homologous membrane protein structures, earlier versions of which were called HOMEP. The code used to compile the dataset is now in the Python programming language, is more streamlined and can run in parallel, allowing for fast future updates as the database of available membrane protein structures continues its pseudo-exponential growth. These changes will facilitate retraining of, and therefore improvements in, our sequence alignment software, AlignMe developed previously. Second, we have expanded upon an earlier (manual) analysis of symmetries in structures of membrane proteins. We first assessed the reliability of available symmetry-detection approaches, by developing a benchmark set of symmetry-containing proteins (called MemSTATS), and testing available methods for their accuracy at detecting these symmetries. We then developed a protocol based on two of these available symmetry analysis tools (SymD and CEsymm), and apply it systematically to known protein structures. This approach is expected to identify patterns and relationships in symmetrical and asymmetrical membrane proteins and to reveal how those symmetries relate to functional mechanisms. The symmetry datasets and all relevant code have been made available through an online data repository. These two analyses have now been combined into a single database called EncoMPASS (Encyclopedia for Membrane Proteins Analyzed by Structure and Symmetry). We have recently made steps to integrate the software underlying the two components of the database. The combination allows us to leverage information about structural neighbors in order to improve the quality and applicability of the symmetry analysis. Moreover, with the aid of the NINDS intramural Bioinformatics staff (Yavaktar and Kumar), we have made visualization of the data fully accessible through a public webserver hosted at https://encompass.ninds.nih.gov. In the past year, we have substantially expanded the search functions for the database, as well as improving the usability and functionality of the web interface. See Ref. 1. In parallel to these studies, we have demonstrated the use of an advanced molecular simulation methodology called EBmetaD developed at NHBLI in the Faraldo-Gomez lab, for providing a quantitative interpretation of electron paramagnetic resonance distance distributions. We applied this method to data obtained by our collaborators (Ziegler, Regensburg, and Prisner, Frankfurt) in simulations of known structures of a betaine transporter, which enabled us to distinguish between states that are likely to be present in the experimental ensemble under physiologically-relevant conditions; ref. 2. This method should be useful for defining the states present in conformational cycles of neuronal transporters such as SERT and DAT.